Random Forest with Attribute Profile for Remote Sensing Image Classification
Paper ID : 1087-MVIP2020
Maryam Imani *
Faculty of Electrical and Computer Engineering Tarbiat Modares University
Although hyperspectral images contain rich spectral information due to high number of spectral bands acquired in a wide and continous range of wavelengths, there are also worthful spatial features in adjacent regions, i.e., neighboring pixels. The extended multi-attribute profile (EMAP) are used for spatial feature extraction in this work. The performance of EMAP is assessed when it fed to the random forest classifier in different scenarios. The only use of EMAP and also fusion of it with spectral features in both cases of full bands and reduced dimensionality are investigated. The advanced binary ant colony optimization is used for implementation of feature reduction. Three fusion frameworks are introduced for integration of EMAP and the spectral bands and the classification results are discussed compared to the only use of EMAP. The experimental results on three popular hyperspectral images show the superior performance of EMAP features fed to the random forest classifier.
random forest, attribute profile, hyperspectral image, spectral-spatial fusion, classification
Status : Paper Accepted (Oral Presentation)